Serious and fatal firearm injuries among children and adolescents in Alaska: 1991-1997.
Bibliographic record
Abstract
STUDY OBJECTIVE: To describe demographics, causal factors, intent, and incident locations of serious and fatal firearm injuries among children and adolescents in Alaska, for the years 1991 through 1997. METHODS: Data from the Alaska Trauma Registry plus Vital Statistics death certificates were reviewed for a seven-year period (1991-1997). Data elements included are: intent (ICD 9-CM E-Codes and narratives); age group; region of incident; place of occurrence; alcohol or drug involvement; type of firearm used; and perpetrator. RESULTS: During the seven-year study period, 222 children and adolescents ages 0-19 years were admitted to a hospital for a non-fatal firearm injury, plus 165 others received fatal firearm injuries. Of these 387 serious and fatal injuries, 34.9% (135) were determined to be unintentional, 36.4% (141) were suicides or suicide attempts, 23.3% (90) were homicide/assaults, 0.5% (2) were legal intervention, and for 4.9% (19) intent was unknown. Rates of serious and fatal firearm injuries per 100,000 youth for the six-year study period ranged from 14 in the Fairbanks North Star Borough and the Kenai Peninsula Borough to 105 in the Yukon-Kuskokwim Region. The statewide average for this period was 27.1 per 100,000 children and adolescents. CONCLUSIONS: Firearm injuries are a leading cause of serious and fatal injuries to children and youth in Alaska. This study suggests that many children and adolescents in Alaska who were injured by firearms, or who caused injury to other children or youth by firearms, had easy access to them. Efforts should be made to convince adults not to let children or at risk teenagers have unsupervised access to firearms, and to promote safe storage of firearms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".